Retail Data Scientist — P2M ML & Optimization

Gap Inc; New York (NY); On-site; USD 147,000 - 191,000; Full time

Benefits offered by this job

Merchandise discount

Competitive PTO

Volunteer hours

401(k) matching

Employee stock purchase plan

Medical, dental, vision and life保险

Job summary

Gap Inc. is seeking a Data Scientist to join our Product-to-Market (P2M) Data Science team.

P2M encompasses the decisions that shape how products move from planning to the customer, including what products to carry, how much inventory to buy, where and how inventory should be positioned, and how products should be priced and promoted. Our team applies machine learning, optimization, and analytics to improve these decisions across P2M lifecycle.

Qualifications

• Master's degree in quantitative field.
• 2–3 years of data science or related experience.
• Experience with retail, supply chain, pricing, inventory, merchandising, or related problems.
• Strong Python and SQL skills.

Responsibilities

• Develop and test machine learning, optimization, and statistical models for retail P2M problems.
• Analyze large datasets and develop analytical solutions using Python, SQL, and related technologies.
• Write reliable, maintainable code and contribute to testing, documentation, and code management.
• Evaluate model performance and support measurement of business impact using appropriate statistical methods.
• Work with senior data scientists to troubleshoot, improve, and operationalize analytical solutions.
• Collaborate with business, product, engineering, and data science partners to understand requirements and communicate analytical findings.

Skills

Python

SQL

Machine learning

Statistics

Data analysis

Cross-functional collaboration

Education

Master's degree in quantitative field

Tools

Gurobi

Pyomo

OR-Tools

PySpark

Job description

Gap Inc. is seeking a Data Scientist to join our Product-to-Market (P2M) Data Science team.

P2M encompasses the decisions that shape how products move from planning to the customer, including what products to carry, how much inventory to buy, where and how inventory should be positioned, and how products should be priced and promoted. Our team applies machine learning, optimization, and analytics to improve these decisions across P2M lifecycle.